In [1]:
from google.colab import drive
drive.mount('/content/drive')
Mounted at /content/drive
In [2]:
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
  print('Not connected to a GPU')
else:
  print(gpu_info)
Wed Dec 21 10:08:15 2022       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   44C    P0    26W /  70W |      0MiB / 15109MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+
In [3]:
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))

if ram_gb < 20:
  print('Not using a high-RAM runtime')
else:
  print('You are using a high-RAM runtime!')
Your runtime has 27.3 gigabytes of available RAM

You are using a high-RAM runtime!
In [ ]:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    pass
#     for filename in filenames:
#         print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

Download yoloV5

In [4]:
!pip install kaggle

from google.colab import files
files.upload()
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)
Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)
Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)
Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)
Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)
Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving kaggle.json to kaggle.json
Out[4]:
{'kaggle.json': b'{"username":"jabullae","key":"9615fe6e84855ac0d22aa288ab5d10bd"}'}
In [5]:
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 ~/.kaggle/kaggle.json
In [6]:
! kaggle datasets download -d kneroma/tacotrashdataset
Downloading tacotrashdataset.zip to /content
 99% 2.77G/2.79G [00:18<00:00, 216MB/s]
100% 2.79G/2.79G [00:18<00:00, 161MB/s]
In [11]:
! unzip /content/tacotrashdataset.zip
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In [13]:
%cd /content/drive/MyDrive/MiniProject
/content/drive/MyDrive/MiniProject
In [14]:
!ls 
 best-checkpoint-003epoch.bin   kaggle.json   tacotrashdataset.zip
 data			        kle_log.txt
'딥러닝 미니프로젝트.ipynb'     meta_df.csv
In [16]:
!git clone https://github.com/ultralytics/yolov5  # clone
%cd /content/drive/MyDrive/MiniProject/yolov5
%pip install -qr requirements.txt  # install

# %pip install torch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0

import torch
from yolov5 import utils
display = utils.notebook_init()  # checks
print (torch.__version__)
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)
Setup complete ✅ (4 CPUs, 25.5 GB RAM, 28.1/166.8 GB disk)
1.13.0+cu116
In [17]:
!ls
benchmarks.py	 data	     LICENSE	      requirements.txt	tutorial.ipynb
CITATION.cff	 detect.py   models	      segment		utils
classify	 export.py   README.md	      setup.cfg		val.py
CONTRIBUTING.md  hubconf.py  README.zh-CN.md  train.py		yolov5
In [18]:
# !pip uninstall typing -y
!pip install pycocotools
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: pycocotools in /usr/local/lib/python3.8/dist-packages (2.0.6)
Requirement already satisfied: matplotlib>=2.1.0 in /usr/local/lib/python3.8/dist-packages (from pycocotools) (3.2.2)
Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from pycocotools) (1.21.6)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (1.4.4)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (2.8.2)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (0.11.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib>=2.1.0->pycocotools) (3.0.9)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.1->matplotlib>=2.1.0->pycocotools) (1.15.0)
In [19]:
!pip install torch torchvision torchaudio 
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.0+cu116)
Requirement already satisfied: torchvision in /usr/local/lib/python3.8/dist-packages (0.14.0+cu116)
Requirement already satisfied: torchaudio in /usr/local/lib/python3.8/dist-packages (0.13.0+cu116)
Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)
Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from torchvision) (2.23.0)
Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from torchvision) (1.21.6)
Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.8/dist-packages (from torchvision) (7.1.2)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2022.12.7)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->torchvision) (1.24.3)
In [20]:
import os
import shutil
import numpy as np
import tqdm
In [21]:
!ls
benchmarks.py	 data	     LICENSE	      requirements.txt	tutorial.ipynb
CITATION.cff	 detect.py   models	      segment		utils
classify	 export.py   README.md	      setup.cfg		val.py
CONTRIBUTING.md  hubconf.py  README.zh-CN.md  train.py		yolov5

Read the annotations file

In [25]:
%cd /content/drive/MyDrive/MiniProject
/content/drive/MyDrive/MiniProject
In [27]:
from pycocotools.coco import COCO
data_source = COCO(annotation_file='data/annotations.json')
loading annotations into memory...
Done (t=0.34s)
creating index...
index created!
In [28]:
catIds = data_source.getCatIds()
cats = data_source.loadCats(catIds)
In [29]:
cats
Out[29]:
[{'supercategory': 'Aluminium foil', 'id': 0, 'name': 'Aluminium foil'},
 {'supercategory': 'Battery', 'id': 1, 'name': 'Battery'},
 {'supercategory': 'Blister pack', 'id': 2, 'name': 'Aluminium blister pack'},
 {'supercategory': 'Blister pack', 'id': 3, 'name': 'Carded blister pack'},
 {'supercategory': 'Bottle', 'id': 4, 'name': 'Other plastic bottle'},
 {'supercategory': 'Bottle', 'id': 5, 'name': 'Clear plastic bottle'},
 {'supercategory': 'Bottle', 'id': 6, 'name': 'Glass bottle'},
 {'supercategory': 'Bottle cap', 'id': 7, 'name': 'Plastic bottle cap'},
 {'supercategory': 'Bottle cap', 'id': 8, 'name': 'Metal bottle cap'},
 {'supercategory': 'Broken glass', 'id': 9, 'name': 'Broken glass'},
 {'supercategory': 'Can', 'id': 10, 'name': 'Food Can'},
 {'supercategory': 'Can', 'id': 11, 'name': 'Aerosol'},
 {'supercategory': 'Can', 'id': 12, 'name': 'Drink can'},
 {'supercategory': 'Carton', 'id': 13, 'name': 'Toilet tube'},
 {'supercategory': 'Carton', 'id': 14, 'name': 'Other carton'},
 {'supercategory': 'Carton', 'id': 15, 'name': 'Egg carton'},
 {'supercategory': 'Carton', 'id': 16, 'name': 'Drink carton'},
 {'supercategory': 'Carton', 'id': 17, 'name': 'Corrugated carton'},
 {'supercategory': 'Carton', 'id': 18, 'name': 'Meal carton'},
 {'supercategory': 'Carton', 'id': 19, 'name': 'Pizza box'},
 {'supercategory': 'Cup', 'id': 20, 'name': 'Paper cup'},
 {'supercategory': 'Cup', 'id': 21, 'name': 'Disposable plastic cup'},
 {'supercategory': 'Cup', 'id': 22, 'name': 'Foam cup'},
 {'supercategory': 'Cup', 'id': 23, 'name': 'Glass cup'},
 {'supercategory': 'Cup', 'id': 24, 'name': 'Other plastic cup'},
 {'supercategory': 'Food waste', 'id': 25, 'name': 'Food waste'},
 {'supercategory': 'Glass jar', 'id': 26, 'name': 'Glass jar'},
 {'supercategory': 'Lid', 'id': 27, 'name': 'Plastic lid'},
 {'supercategory': 'Lid', 'id': 28, 'name': 'Metal lid'},
 {'supercategory': 'Other plastic', 'id': 29, 'name': 'Other plastic'},
 {'supercategory': 'Paper', 'id': 30, 'name': 'Magazine paper'},
 {'supercategory': 'Paper', 'id': 31, 'name': 'Tissues'},
 {'supercategory': 'Paper', 'id': 32, 'name': 'Wrapping paper'},
 {'supercategory': 'Paper', 'id': 33, 'name': 'Normal paper'},
 {'supercategory': 'Paper bag', 'id': 34, 'name': 'Paper bag'},
 {'supercategory': 'Paper bag', 'id': 35, 'name': 'Plastified paper bag'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 36, 'name': 'Plastic film'},
 {'supercategory': 'Plastic bag & wrapper',
  'id': 37,
  'name': 'Six pack rings'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 38, 'name': 'Garbage bag'},
 {'supercategory': 'Plastic bag & wrapper',
  'id': 39,
  'name': 'Other plastic wrapper'},
 {'supercategory': 'Plastic bag & wrapper',
  'id': 40,
  'name': 'Single-use carrier bag'},
 {'supercategory': 'Plastic bag & wrapper',
  'id': 41,
  'name': 'Polypropylene bag'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 42, 'name': 'Crisp packet'},
 {'supercategory': 'Plastic container', 'id': 43, 'name': 'Spread tub'},
 {'supercategory': 'Plastic container', 'id': 44, 'name': 'Tupperware'},
 {'supercategory': 'Plastic container',
  'id': 45,
  'name': 'Disposable food container'},
 {'supercategory': 'Plastic container',
  'id': 46,
  'name': 'Foam food container'},
 {'supercategory': 'Plastic container',
  'id': 47,
  'name': 'Other plastic container'},
 {'supercategory': 'Plastic glooves', 'id': 48, 'name': 'Plastic glooves'},
 {'supercategory': 'Plastic utensils', 'id': 49, 'name': 'Plastic utensils'},
 {'supercategory': 'Pop tab', 'id': 50, 'name': 'Pop tab'},
 {'supercategory': 'Rope & strings', 'id': 51, 'name': 'Rope & strings'},
 {'supercategory': 'Scrap metal', 'id': 52, 'name': 'Scrap metal'},
 {'supercategory': 'Shoe', 'id': 53, 'name': 'Shoe'},
 {'supercategory': 'Squeezable tube', 'id': 54, 'name': 'Squeezable tube'},
 {'supercategory': 'Straw', 'id': 55, 'name': 'Plastic straw'},
 {'supercategory': 'Straw', 'id': 56, 'name': 'Paper straw'},
 {'supercategory': 'Styrofoam piece', 'id': 57, 'name': 'Styrofoam piece'},
 {'supercategory': 'Unlabeled litter', 'id': 58, 'name': 'Unlabeled litter'},
 {'supercategory': 'Cigarette', 'id': 59, 'name': 'Cigarette'}]
In [30]:
trash = ['Cigarette', 'Unlabeled litter', 'Other plastic wrapper', 'Plastic straw', 'Aluminium foil', 'Tissues',
         'Rope & strings', 'Aluminium blister pack', 'Paper straw', 'Plastic glooves', 'Shoe']
plastic = ['Plastic film', 'Clear plastic bottle', 'Other plastic', 'Plastic bottle cap', 'Disposable plastic cup',
           'Spread tub', 'Other plastic bottle', 'Plastic lid', 'Disposable food container', 'Other plastic cup',
           'Carded blister pack', 'Plastic utensils', 'Squeezable tube', 'Other plastic container', 'Six pack rings',
           'Tupperware', 'Polypropylene bag']
metal = ['Drink can', 'Pop tab', 'Metal bottle cap', 'Food Can', 'Aerosol', 'Metal lid', 'Scrap metal']
glass = ['Broken glass', 'Glass bottle', 'Glass jar', 'Glass cup']
styrofoam = ['Styrofoam piece', 'Foam food container', 'Foam cup']
paper = ['Other carton', 'Plastified paper bag', 'Normal paper', 'Meal carton','Paper cup', 'Corrugated carton', 'Wrapping paper',
         'Toilet tube', 'Magazine paper', 'Egg carton', 'Paper bag', 'Drink carton', 'Pizza box']
vinyl = ['Single-use carrier bag', 'Crisp packet', 'Garbage bag']
food_waste = ['food waste']
battery = ['Battery']

for i in cats:
  if i['name'] in trash:
    i['name'] = 'trash'
    i['id'] = 0
  if i['name'] in plastic:
    i['name'] = 'plastic'
    i['id'] = 1
  if i['name'] in metal:
    i['name'] = 'metal'
    i['id'] = 2
  if i['name'] in glass:
    i['name'] = 'glass'
    i['id'] = 3
  if i['name'] in styrofoam:
    i['name'] = 'styrofoam'
    i['id'] = 4
  if i['name'] in paper:
    i['name'] = 'paper'
    i['id'] = 5
  if i['name'] in vinyl:
    i['name'] = 'vinyl'
    i['id'] = 6
  if i['name'] in food_waste:
    i['name'] = 'food waste'
    i['id'] = 7
  if i['name'] in battery:
    i['name'] = 'battery'
    i['id'] = 8
In [31]:
cats
Out[31]:
[{'supercategory': 'Aluminium foil', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Battery', 'id': 8, 'name': 'battery'},
 {'supercategory': 'Blister pack', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Blister pack', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Bottle', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Bottle', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Bottle', 'id': 3, 'name': 'glass'},
 {'supercategory': 'Bottle cap', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Bottle cap', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Broken glass', 'id': 3, 'name': 'glass'},
 {'supercategory': 'Can', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Can', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Can', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Carton', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Cup', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Cup', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Cup', 'id': 4, 'name': 'styrofoam'},
 {'supercategory': 'Cup', 'id': 3, 'name': 'glass'},
 {'supercategory': 'Cup', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Food waste', 'id': 25, 'name': 'Food waste'},
 {'supercategory': 'Glass jar', 'id': 3, 'name': 'glass'},
 {'supercategory': 'Lid', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Lid', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Other plastic', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Paper', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Paper', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Paper bag', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Paper bag', 'id': 5, 'name': 'paper'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic bag & wrapper', 'id': 6, 'name': 'vinyl'},
 {'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic container', 'id': 4, 'name': 'styrofoam'},
 {'supercategory': 'Plastic container', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Plastic glooves', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Plastic utensils', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Pop tab', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Rope & strings', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Scrap metal', 'id': 2, 'name': 'metal'},
 {'supercategory': 'Shoe', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Squeezable tube', 'id': 1, 'name': 'plastic'},
 {'supercategory': 'Straw', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Straw', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Styrofoam piece', 'id': 4, 'name': 'styrofoam'},
 {'supercategory': 'Unlabeled litter', 'id': 0, 'name': 'trash'},
 {'supercategory': 'Cigarette', 'id': 0, 'name': 'trash'}]

Creating a class Dictionary

  • classes = Mapping of Class Name to ID
In [42]:
annotation
Out[42]:
{'id': 2,
 'image_id': 1,
 'category_id': 18,
 'segmentation': [[928.0,
   1876.0,
   938.0,
   1856.0,
   968.0,
   1826.0,
   990.0,
   1808.0,
   998.0,
   1790.0,
   1069.0,
   1727.0,
   1096.0,
   1702.0,
   1159.0,
   1644.0,
   1212.0,
   1588.0,
   1258.0,
   1540.0,
   1314.0,
   1482.0,
   1357.0,
   1444.0,
   1392.0,
   1416.0,
   1409.0,
   1393.0,
   1430.0,
   1369.0,
   1415.0,
   1347.0,
   1130.0,
   1087.0,
   780.0,
   763.0,
   528.0,
   533.0,
   479.0,
   486.0,
   466.0,
   466.0,
   448.0,
   457.0,
   427.0,
   468.0,
   387.0,
   502.0,
   321.0,
   554.0,
   244.0,
   608.0,
   118.0,
   693.0,
   37.0,
   750.0,
   3.0,
   780.0,
   1.0,
   995.0,
   28.0,
   1032.0,
   104.0,
   1119.0,
   403.0,
   1471.0,
   666.0,
   1805.0,
   763.0,
   1954.0,
   782.0,
   1945.0,
   796.0,
   1970.0,
   803.0,
   1976.0,
   818.0,
   1976.0,
   836.0,
   1956.0,
   852.0,
   1954.0,
   860.0,
   1937.0,
   873.0,
   1931.0,
   885.0,
   1908.0,
   898.0,
   1896.0,
   928.0,
   1876.0]],
 'area': 1071259.5,
 'bbox': [1.0, 457.0, 1429.0, 1519.0],
 'iscrowd': 0}
In [43]:
classes = {'trash' : 0,
           'plastic' : 1,
           'metal' : 2,
           'glass' : 3,
           'styrofoam' : 4,
           'paper' : 5,
           'vinyl' : 6,
           'food waste' : 7,
           'battery' : 8}
taco_labels = {0:0,
               1:1,
               2:2,
               3:3,
               4:4,
               5:5,
               6:6,
               7:7,
               8:8,
               }
taco_labels_inverse = {0:0,
                      1:8,
                      2:0,
                      3:1,
                      4:1,
                      5:1,
                      6:3,
                      7:1,
                      8:2,
                       9:3,
                       10:2,
                       11:2,
                       12:2,
                       13:5,
                       14:5,
                       15:5,
                       16:5,
                       17:5,
                       18:5,
                       19:5,
                       20:5,
                       21:1,
                       22:4,
                       23:3,
                       24:1,
                       25:7,
                       26:3,
                       27:1,
                       28:2,
                       29:1,
                       30:5,
                       31:0,
                       32:5,
                       33:5,
                       34:5,
                       35:5,
                       36:1,
                       37:1,
                       38:6,
                       39:0,
                       40:6,
                       41:1,
                       42:6,
                       43:1,
                       44:1,
                       45:1,
                       46:4,
                       47:1,
                       48:0,
                       49:1,
                       50:2,
                       51:0,
                       52:2,
                       53:0,
                       54:1,
                       55:0,
                       56:0,
                       57:4,
                       58:0,
                       59:0}
In [34]:
# classes = {}
# taco_labels = {}
# taco_labels_inverse = {}

# for c in cats:
#     taco_labels[len(classes)] = c['id']
#     taco_labels_inverse[c['id']] = len(classes)
#     classes[c['name']] = len(classes)

Splitting data into train, val and test

In [35]:
!ls
 best-checkpoint-003epoch.bin   kaggle.json   tacotrashdataset.zip
 data			        kle_log.txt   yolov5
'딥러닝 미니프로젝트.ipynb'     meta_df.csv
In [36]:
%cd /content/drive/MyDrive/MiniProject/yolov5
!mkdir -p tmp/labels tmp/images
IMAGES_PATH = 'tmp/images/'
LABELS_PATH = 'tmp/labels/'
/content/drive/MyDrive/MiniProject/yolov5
In [37]:
!ls tmp
images	labels
In [38]:
import shutil
import os
In [44]:
imgIds = data_source.getImgIds()
# print(data_source.loadImgs(0)[0])
for index, img_id in tqdm.tqdm(enumerate(imgIds)):
    img_info = data_source.loadImgs(img_id)[0]
      # img_dir: batch_x/.....jpg ---> batch_x_......jpg
    img_dir = img_info['file_name'].replace('/', '-')
    
    image_name = img_dir.split('.')[0]
    label_dir = LABELS_PATH + image_name + '.txt'
    
    height = img_info['height']
    width = img_info['width']
    
#     print ("Copying from /kaggle/input/tacotrashdataset/data/{} to {}".format(img_info['file_name'], os.path.join(IMAGES_PATH, img_dir)))
    # get images
    shutil.copy(f"/content/drive/MyDrive/MiniProject/data/{img_info['file_name']}", os.path.join(IMAGES_PATH, img_dir))
        

    
      # get labels
    with open(label_dir, mode='w') as fp:
#         print (f"Creating label_dir {label_dir} for {image_name}")
        annotation_id = data_source.getAnnIds(img_id)
        if len(annotation_id) == 0:
            fp.write('')
            continue
        boxes = np.zeros((0, 5))
        annotations = data_source.loadAnns(annotation_id)
        lines = ''
        for annotation in annotations:
            label = taco_labels_inverse[annotation['category_id']]
            box = annotation['bbox']
            # some annotations have basically no width / height (extremely small), skip them
            if box[2] < 1 or box[3] < 1:
                continue
            # top_x,top_y,width,height ----> cen_x,cen_y,width,height
            # standardize to 0-1
            box[0] = round((box[0] + box[2] / 2) / width, 6)
            box[1] = round((box[1] + box[3] / 2) / height, 6)
            box[2] = round(box[2] / width, 6)
            box[3] = round(box[3] / height, 6)
            # line: label x_center y_center width height
            lines = lines + str(label)
            for i in box:
                lines += ' ' + str(i)
            lines += '\n'
        fp.writelines(lines)
1500it [06:53,  3.63it/s]
In [45]:
!ls
benchmarks.py	 detect.py   README.md	       tmp	       yolov5
CITATION.cff	 export.py   README.zh-CN.md   train.py
classify	 hubconf.py  requirements.txt  tutorial.ipynb
CONTRIBUTING.md  LICENSE     segment	       utils
data		 models      setup.cfg	       val.py
In [46]:
# test
annotation_id = data_source.getAnnIds(1)
data_source.loadAnns(annotation_id)
Out[46]:
[{'id': 2,
  'image_id': 1,
  'category_id': 18,
  'segmentation': [[928.0,
    1876.0,
    938.0,
    1856.0,
    968.0,
    1826.0,
    990.0,
    1808.0,
    998.0,
    1790.0,
    1069.0,
    1727.0,
    1096.0,
    1702.0,
    1159.0,
    1644.0,
    1212.0,
    1588.0,
    1258.0,
    1540.0,
    1314.0,
    1482.0,
    1357.0,
    1444.0,
    1392.0,
    1416.0,
    1409.0,
    1393.0,
    1430.0,
    1369.0,
    1415.0,
    1347.0,
    1130.0,
    1087.0,
    780.0,
    763.0,
    528.0,
    533.0,
    479.0,
    486.0,
    466.0,
    466.0,
    448.0,
    457.0,
    427.0,
    468.0,
    387.0,
    502.0,
    321.0,
    554.0,
    244.0,
    608.0,
    118.0,
    693.0,
    37.0,
    750.0,
    3.0,
    780.0,
    1.0,
    995.0,
    28.0,
    1032.0,
    104.0,
    1119.0,
    403.0,
    1471.0,
    666.0,
    1805.0,
    763.0,
    1954.0,
    782.0,
    1945.0,
    796.0,
    1970.0,
    803.0,
    1976.0,
    818.0,
    1976.0,
    836.0,
    1956.0,
    852.0,
    1954.0,
    860.0,
    1937.0,
    873.0,
    1931.0,
    885.0,
    1908.0,
    898.0,
    1896.0,
    928.0,
    1876.0]],
  'area': 1071259.5,
  'bbox': [0.465517, 0.593704, 0.929733, 0.741337],
  'iscrowd': 0},
 {'id': 3,
  'image_id': 1,
  'category_id': 14,
  'segmentation': [[617.0,
    383.0,
    703.0,
    437.0,
    713.0,
    456.0,
    725.0,
    459.0,
    747.0,
    482.0,
    760.0,
    483.0,
    780.0,
    506.0,
    794.0,
    520.0,
    807.0,
    528.0,
    827.0,
    537.0,
    835.0,
    551.0,
    852.0,
    555.0,
    882.0,
    576.0,
    913.0,
    596.0,
    929.0,
    605.0,
    954.0,
    617.0,
    972.0,
    622.0,
    998.0,
    630.0,
    1034.0,
    640.0,
    1051.0,
    644.0,
    1064.0,
    632.0,
    1081.0,
    616.0,
    1104.0,
    589.0,
    1121.0,
    576.0,
    1152.0,
    566.0,
    1177.0,
    564.0,
    1201.0,
    569.0,
    1231.0,
    589.0,
    1260.0,
    613.0,
    1277.0,
    644.0,
    1298.0,
    669.0,
    1318.0,
    694.0,
    1343.0,
    724.0,
    1362.0,
    756.0,
    1378.0,
    779.0,
    1389.0,
    795.0,
    1389.0,
    801.0,
    1398.0,
    811.0,
    1415.0,
    821.0,
    1427.0,
    837.0,
    1437.0,
    848.0,
    1450.0,
    863.0,
    1461.0,
    872.0,
    1469.0,
    887.0,
    1483.0,
    898.0,
    1495.0,
    927.0,
    1501.0,
    949.0,
    1506.0,
    964.0,
    1537.0,
    917.0,
    1536.0,
    822.0,
    1522.0,
    790.0,
    1512.0,
    783.0,
    1497.0,
    768.0,
    1479.0,
    751.0,
    1459.0,
    738.0,
    1428.0,
    695.0,
    1403.0,
    653.0,
    1370.0,
    610.0,
    1351.0,
    589.0,
    1342.0,
    585.0,
    1338.0,
    570.0,
    1328.0,
    558.0,
    1305.0,
    532.0,
    1276.0,
    505.0,
    1256.0,
    501.0,
    1250.0,
    491.0,
    1235.0,
    492.0,
    1206.0,
    497.0,
    1180.0,
    501.0,
    1157.0,
    520.0,
    1104.0,
    565.0,
    1091.0,
    574.0,
    1062.0,
    571.0,
    1028.0,
    560.0,
    998.0,
    551.0,
    943.0,
    523.0,
    886.0,
    485.0,
    812.0,
    427.0,
    746.0,
    368.0,
    710.0,
    346.0,
    667.0,
    320.0,
    642.0,
    308.0,
    636.0,
    293.0,
    618.0,
    293.0,
    594.0,
    295.0,
    561.0,
    292.0,
    545.0,
    300.0,
    531.0,
    312.0,
    536.0,
    326.0,
    549.0,
    346.0,
    562.0,
    357.0,
    594.0,
    371.0,
    617.0,
    383.0]],
  'area': 99583.5,
  'bbox': [0.672739, 0.306491, 0.654522, 0.327965],
  'iscrowd': 0}]
In [47]:
print(len(os.listdir(IMAGES_PATH)))
print(len(os.listdir(LABELS_PATH)))
1500
1500
In [48]:
!pip install split-folders
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting split-folders
  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)
Installing collected packages: split-folders
Successfully installed split-folders-0.5.1
In [51]:
import splitfolders
%cd /content/drive/MyDrive/MiniProject/yolov5
splitfolders.ratio('tmp', output='taco', seed=1337, ratio=(.8, 0.1,0.1)) 
/content/drive/MyDrive/MiniProject/yolov5
Copying files: 3000 files [00:40, 74.52 files/s]
In [52]:
!ls taco
test  train  val
In [53]:
# check files
print(sorted(os.listdir('taco/train/images'))[:5])
print(sorted(os.listdir('taco/val/images'))[:5])
print(sorted(os.listdir('taco/test/images'))[:5])
['batch_1-000001.jpg', 'batch_1-000003.jpg', 'batch_1-000004.jpg', 'batch_1-000005.jpg', 'batch_1-000007.jpg']
['batch_1-000000.jpg', 'batch_1-000006.jpg', 'batch_1-000016.jpg', 'batch_1-000030.jpg', 'batch_1-000038.jpg']
['batch_1-000029.jpg', 'batch_1-000045.jpg', 'batch_1-000047.jpg', 'batch_1-000065.JPG', 'batch_1-000108.JPG']
In [54]:
#customize iPython writefile so we can write variables
from IPython.core.magic import register_line_cell_magic

@register_line_cell_magic
def writetemplate(line, cell):
    print(line)
    with open(line, 'w') as f:
        f.write(cell.format(**globals()))
In [55]:
%cd /content/drive/MyDrive/MiniProject/yolov5
!ls
/content/drive/MyDrive/MiniProject/yolov5
benchmarks.py	 detect.py   README.md	       taco	       val.py
CITATION.cff	 export.py   README.zh-CN.md   tmp	       yolov5
classify	 hubconf.py  requirements.txt  train.py
CONTRIBUTING.md  LICENSE     segment	       tutorial.ipynb
data		 models      setup.cfg	       utils
In [57]:
%%writetemplate data/taco9.yaml

train: taco/train/images
val:   taco/val/images

# number of classes
nc: 9

# class names
names: ['trash',
        'plastic',
        'metal',
        'glass',
        'styrofoam',
        'paper',
        'vinyl',
        'food waste',
        'battery']
data/taco9.yaml
In [58]:
!pwd
/content/drive/MyDrive/MiniProject/yolov5
In [61]:
%cd /content/drive/MyDrive/MiniProject/yolov5
# %cd yolov5
%cat data/taco9.yaml
/content/drive/MyDrive/MiniProject/yolov5

train: taco/train/images
val:   taco/val/images

# number of classes
nc: 9

# class names
names: ['trash',
        'plastic',
        'metal',
        'glass',
        'styrofoam',
        'paper',
        'vinyl',
        'food waste',
        'battery']
In [62]:
# Removing the tmp folder

!rm -r tmp
In [63]:
!wandb disabled
/bin/bash: wandb: command not found
In [64]:
!pwd
/content/drive/MyDrive/MiniProject/yolov5
In [ ]:
import time
start = time.time()
!python train.py --img 640 --batch 16 --epochs 10 --data taco9.yaml --weights yolov5s.pt

end = time.time() 
train: weights=yolov5s.pt, cfg=, data=taco9.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 57.6MB/s]

Overriding model.yaml nc=80 with nc=9

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 
  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1     37758  models.yolo.Detect                      [9, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model summary: 214 layers, 7043902 parameters, 7043902 gradients, 16.0 GFLOPs

Transferred 343/349 items from yolov5s.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
train: Scanning /content/drive/MyDrive/MiniProject/yolov5/taco/train/labels... 1200 images, 1 backgrounds, 0 corrupt: 100% 1200/1200 [00:03<00:00, 320.10it/s]
train: New cache created: /content/drive/MyDrive/MiniProject/yolov5/taco/train/labels.cache
val: Scanning /content/drive/MyDrive/MiniProject/yolov5/taco/val/labels... 150 images, 1 backgrounds, 0 corrupt: 100% 150/150 [00:00<00:00, 151.36it/s]
val: New cache created: /content/drive/MyDrive/MiniProject/yolov5/taco/val/labels.cache

AutoAnchor: 4.23 anchors/target, 0.984 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp/labels.jpg... 
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs/train/exp
Starting training for 10 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/9      3.76G     0.1225    0.03524    0.06476         54        640:  16% 12/75 [00:47<01:58,  1.89s/it]
In [ ]:
 
In [ ]:
print (f"Time Elapsed: {end-start}")
Time Elapsed: 3113.0753841400146

See results

In [ ]:
%cd /content/yolov5/runs/train/exp3/
/content/yolov5/runs/train/exp3
In [ ]:
import matplotlib.pyplot as plt
res_path = 'results.png'
img = plt.imread(res_path)
plt.figure(figsize=(20, 20))
plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.show()
In [ ]:
import pandas as pd

pd.read_csv('results.csv')
Out[ ]:
epoch train/box_loss train/obj_loss train/cls_loss metrics/precision ... val/obj_loss val/cls_loss x/lr0 x/lr1 x/lr2
0 0 0.104530 0.035835 0.059713 0.52692 ... 0.026340 0.053226 0.070400 0.003289 0.003289
1 1 0.074191 0.033781 0.049640 0.31868 ... 0.021053 0.047766 0.039089 0.005311 0.005311
2 2 0.064907 0.030058 0.046801 0.62166 ... 0.019943 0.046435 0.006458 0.006013 0.006013
3 3 0.055808 0.028955 0.046160 0.61846 ... 0.020071 0.045666 0.004060 0.004060 0.004060
4 4 0.053244 0.029055 0.044466 0.61693 ... 0.019931 0.045358 0.004060 0.004060 0.004060

5 rows × 14 columns

In [ ]:
!cat /content/yolov5/runs/train/exp3/results.csv
               epoch,      train/box_loss,      train/obj_loss,      train/cls_loss,   metrics/precision,      metrics/recall,     metrics/mAP_0.5,metrics/mAP_0.5:0.95,        val/box_loss,        val/obj_loss,        val/cls_loss,               x/lr0,               x/lr1,               x/lr2
                   0,             0.10453,            0.035835,            0.059713,             0.52692,            0.090024,            0.021278,           0.0057413,            0.087463,             0.02634,            0.053226,              0.0704,           0.0032889,           0.0032889
                   1,            0.074191,            0.033781,             0.04964,             0.31868,             0.14579,            0.072828,            0.036463,            0.071678,            0.021053,            0.047766,            0.039089,            0.005311,            0.005311
                   2,            0.064907,            0.030058,            0.046801,             0.62166,             0.13176,            0.093548,            0.044356,            0.062293,            0.019943,            0.046435,           0.0064576,           0.0060132,           0.0060132
                   3,            0.055808,            0.028955,             0.04616,             0.61846,             0.17549,             0.12195,             0.06479,            0.051539,            0.020071,            0.045666,             0.00406,             0.00406,             0.00406
                   4,            0.053244,            0.029055,            0.044466,             0.61693,             0.17788,             0.12095,             0.06385,            0.048399,            0.019931,            0.045358,             0.00406,             0.00406,             0.00406
In [ ]:
import os
os.chdir(r'/content/yolov5/runs/train/exp3')
from IPython.display import FileLink
FileLink(r'results.csv')
Out[ ]:
In [ ]:
%cd /content/yolov5
/content/yolov5
In [ ]:
# !cp runs/exp/weights/best.pt weights
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source taco/test/images 
detect: weights=['runs/train/exp3/weights/best.pt'], source=taco/test/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB)

Fusing layers... 
Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs
image 1/150 /content/yolov5/taco/test/images/batch_1-000029.jpg: 640x480 (no detections), 16.4ms
image 2/150 /content/yolov5/taco/test/images/batch_1-000045.jpg: 480x640 2 papers, 16.7ms
image 3/150 /content/yolov5/taco/test/images/batch_1-000047.jpg: 480x640 2 plastics, 4 papers, 10.8ms
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image 48/150 /content/yolov5/taco/test/images/batch_14-000041.jpg: 640x480 (no detections), 10.8ms
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image 74/150 /content/yolov5/taco/test/images/batch_3-IMG_4950.JPG: 640x608 1 plastic, 1 paper, 17.1ms
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image 143/150 /content/yolov5/taco/test/images/batch_9-000029.jpg: 640x480 1 trash, 3 plastics, 13.9ms
image 144/150 /content/yolov5/taco/test/images/batch_9-000031.jpg: 640x480 (no detections), 10.6ms
image 145/150 /content/yolov5/taco/test/images/batch_9-000039.jpg: 640x480 (no detections), 10.7ms
image 146/150 /content/yolov5/taco/test/images/batch_9-000053.jpg: 480x640 2 plastics, 11.5ms
image 147/150 /content/yolov5/taco/test/images/batch_9-000061.jpg: 640x480 1 plastic, 11.6ms
image 148/150 /content/yolov5/taco/test/images/batch_9-000073.jpg: 640x320 2 plastics, 12.2ms
image 149/150 /content/yolov5/taco/test/images/batch_9-000080.jpg: 640x320 2 plastics, 10.9ms
image 150/150 /content/yolov5/taco/test/images/batch_9-000090.jpg: 640x320 1 plastic, 10.9ms
Speed: 0.6ms pre-process, 11.4ms inference, 0.9ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp
In [ ]:
!pwd
/content/yolov5
In [ ]:
!ls runs/detect/exp
batch_10-000002.jpg  batch_15-000028.jpg   batch_6-000061.JPG
batch_10-000008.jpg  batch_15-000029.jpg   batch_6-000064.JPG
batch_10-000009.jpg  batch_15-000035.jpg   batch_6-000066.JPG
batch_10-000013.jpg  batch_15-000041.jpg   batch_6-000073.JPG
batch_10-000019.jpg  batch_15-000042.jpg   batch_6-000096.JPG
batch_10-000032.jpg  batch_15-000046.jpg   batch_6-000097.JPG
batch_10-000035.jpg  batch_15-000080.jpg   batch_6-000101.JPG
batch_10-000036.jpg  batch_15-000082.jpg   batch_6-000103.JPG
batch_10-000039.jpg  batch_2-000029.JPG    batch_7-000011.JPG
batch_10-000046.jpg  batch_2-000030.JPG    batch_7-000024.JPG
batch_10-000059.jpg  batch_2-000033.JPG    batch_7-000031.JPG
batch_1-000029.jpg   batch_2-000039.JPG    batch_7-000053.JPG
batch_1-000045.jpg   batch_2-000040.JPG    batch_7-000068.JPG
batch_1-000047.jpg   batch_2-000041.JPG    batch_7-000073.JPG
batch_1-000065.JPG   batch_2-000055.JPG    batch_7-000077.JPG
batch_1-000108.JPG   batch_2-000067.JPG    batch_7-000080.JPG
batch_1-000111.JPG   batch_2-000069.JPG    batch_7-000089.JPG
batch_1-000119.JPG   batch_3-IMG_4854.JPG  batch_7-000091.JPG
batch_11-000013.jpg  batch_3-IMG_4862.JPG  batch_7-000094.JPG
batch_11-000022.jpg  batch_3-IMG_4895.JPG  batch_7-000106.JPG
batch_11-000041.jpg  batch_3-IMG_4897.JPG  batch_7-000108.JPG
batch_11-000066.jpg  batch_3-IMG_4901.JPG  batch_7-000110.JPG
batch_11-000077.jpg  batch_3-IMG_4924.JPG  batch_7-000120.JPG
batch_12-000006.jpg  batch_3-IMG_4950.JPG  batch_7-000121.JPG
batch_12-000017.jpg  batch_3-IMG_4971.JPG  batch_7-000141.JPG
batch_12-000026.jpg  batch_3-IMG_5065.JPG  batch_8-000000.jpg
batch_12-000038.jpg  batch_3-IMG_5068.JPG  batch_8-000007.jpg
batch_12-000047.jpg  batch_4-000005.JPG    batch_8-000018.jpg
batch_12-000049.jpg  batch_4-000006.JPG    batch_8-000020.jpg
batch_12-000069.jpg  batch_4-000011.JPG    batch_8-000022.jpg
batch_12-000077.jpg  batch_4-000028.JPG    batch_8-000037.jpg
batch_12-000091.jpg  batch_4-000058.JPG    batch_8-000039.jpg
batch_12-000097.jpg  batch_4-000064.JPG    batch_8-000044.jpg
batch_13-000001.jpg  batch_4-000068.JPG    batch_8-000045.jpg
batch_13-000002.jpg  batch_4-000076.JPG    batch_8-000057.jpg
batch_13-000010.jpg  batch_4-000077.JPG    batch_8-000059.jpg
batch_13-000018.jpg  batch_5-000028.JPG    batch_8-000068.jpg
batch_13-000025.jpg  batch_5-000036.JPG    batch_8-000083.jpg
batch_13-000036.jpg  batch_5-000052.JPG    batch_8-000091.jpg
batch_13-000052.jpg  batch_5-000076.JPG    batch_9-000014.jpg
batch_13-000067.jpg  batch_5-000114.JPG    batch_9-000020.jpg
batch_14-000003.jpg  batch_6-000000.JPG    batch_9-000027.jpg
batch_14-000016.jpg  batch_6-000002.JPG    batch_9-000029.jpg
batch_14-000017.jpg  batch_6-000007.JPG    batch_9-000031.jpg
batch_14-000025.jpg  batch_6-000008.JPG    batch_9-000039.jpg
batch_14-000035.jpg  batch_6-000019.JPG    batch_9-000053.jpg
batch_14-000038.jpg  batch_6-000034.JPG    batch_9-000061.jpg
batch_14-000041.jpg  batch_6-000038.JPG    batch_9-000073.jpg
batch_14-000082.jpg  batch_6-000050.JPG    batch_9-000080.jpg
batch_14-000087.jpg  batch_6-000054.JPG    batch_9-000090.jpg
In [ ]:
import glob
from IPython.display import Image, display

def inspectPerformance(mode, run):
    cnt = 0 
    %cd yolov5
    for imageName in glob.glob('runs/{}/{}/*.jpg'.format(mode,run)): #assuming JPG
        display(Image(filename=imageName))
        print("\n")
        cnt += 1
        if cnt == 5:
            break
In [ ]:
# we use the helper function defined above to view the TEST results from EXP __ (we can see this from the last line in the 
# log output after running the test command)
inspectPerformance("detect", "exp") # change 'exp19' to the name of your test_run
[Errno 2] No such file or directory: 'yolov5'
/content/yolov5


In [ ]:
!python test.py
python: can't open file 'test.py': [Errno 2] No such file or directory

Show Training Result

In [ ]:
%cd '/content/yolov5/runs/train/exp'
%ls
/kaggle/working/yolov5/runs/train/exp
F1_curve.png                                       results.png
PR_curve.png                                       train_batch0.jpg
P_curve.png                                        train_batch1.jpg
R_curve.png                                        train_batch2.jpg
confusion_matrix.png                               val_batch0_labels.jpg
events.out.tfevents.1648357698.0d1bcf56e556.213.0  val_batch0_pred.jpg
hyp.yaml                                           val_batch1_labels.jpg
labels.jpg                                         val_batch1_pred.jpg
labels_correlogram.jpg                             val_batch2_labels.jpg
opt.yaml                                           val_batch2_pred.jpg
results.csv                                        weights/
In [ ]:
inspectPerformance("train", "exp")
[Errno 2] No such file or directory: 'yolov5'
/kaggle/working/yolov5/runs/train/exp
In [ ]:
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source /content/쓰레기.mp4 
detect: weights=['runs/train/exp3/weights/best.pt'], source=/content/쓰레기.mp4, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB)

Fusing layers... 
Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs
video 1/1 (1/124) /content/쓰레기.mp4: 384x640 3 papers, 17.8ms
video 1/1 (2/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms
video 1/1 (3/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms
video 1/1 (4/124) /content/쓰레기.mp4: 384x640 2 papers, 11.5ms
video 1/1 (5/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (6/124) /content/쓰레기.mp4: 384x640 3 papers, 11.5ms
video 1/1 (7/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (8/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms
video 1/1 (9/124) /content/쓰레기.mp4: 384x640 2 papers, 11.2ms
video 1/1 (10/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (11/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms
video 1/1 (12/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms
video 1/1 (13/124) /content/쓰레기.mp4: 384x640 3 papers, 11.6ms
video 1/1 (14/124) /content/쓰레기.mp4: 384x640 3 papers, 11.8ms
video 1/1 (15/124) /content/쓰레기.mp4: 384x640 3 papers, 11.5ms
video 1/1 (16/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms
video 1/1 (17/124) /content/쓰레기.mp4: 384x640 3 papers, 11.6ms
video 1/1 (18/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (19/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms
video 1/1 (20/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (21/124) /content/쓰레기.mp4: 384x640 4 papers, 11.3ms
video 1/1 (22/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms
video 1/1 (23/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms
video 1/1 (24/124) /content/쓰레기.mp4: 384x640 3 papers, 11.3ms
video 1/1 (25/124) /content/쓰레기.mp4: 384x640 3 papers, 11.1ms
video 1/1 (26/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms
video 1/1 (27/124) /content/쓰레기.mp4: 384x640 3 papers, 11.0ms
video 1/1 (28/124) /content/쓰레기.mp4: 384x640 3 papers, 11.4ms
video 1/1 (29/124) /content/쓰레기.mp4: 384x640 4 papers, 11.2ms
video 1/1 (30/124) /content/쓰레기.mp4: 384x640 3 papers, 11.2ms
video 1/1 (31/124) /content/쓰레기.mp4: 384x640 4 papers, 11.1ms
video 1/1 (32/124) /content/쓰레기.mp4: 384x640 4 papers, 11.1ms
video 1/1 (33/124) /content/쓰레기.mp4: 384x640 5 papers, 11.2ms
video 1/1 (34/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms
video 1/1 (35/124) /content/쓰레기.mp4: 384x640 4 papers, 11.3ms
video 1/1 (36/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms
video 1/1 (37/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms
video 1/1 (38/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.4ms
video 1/1 (39/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.7ms
video 1/1 (40/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms
video 1/1 (41/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.5ms
video 1/1 (42/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 12.2ms
video 1/1 (43/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.2ms
video 1/1 (44/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms
video 1/1 (45/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms
video 1/1 (46/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.3ms
video 1/1 (47/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (48/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.0ms
video 1/1 (49/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.5ms
video 1/1 (50/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms
video 1/1 (51/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.1ms
video 1/1 (52/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms
video 1/1 (53/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.3ms
video 1/1 (54/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms
video 1/1 (55/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms
video 1/1 (56/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms
video 1/1 (57/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms
video 1/1 (58/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.7ms
video 1/1 (59/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (60/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (61/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (62/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (63/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (64/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms
video 1/1 (65/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.0ms
video 1/1 (66/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.2ms
video 1/1 (67/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms
video 1/1 (68/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (69/124) /content/쓰레기.mp4: 384x640 3 plastics, 3 papers, 11.1ms
video 1/1 (70/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (71/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.1ms
video 1/1 (72/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (73/124) /content/쓰레기.mp4: 384x640 2 plastics, 2 papers, 11.1ms
video 1/1 (74/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 10.9ms
video 1/1 (75/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (76/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms
video 1/1 (77/124) /content/쓰레기.mp4: 384x640 3 plastics, 4 papers, 10.9ms
video 1/1 (78/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.0ms
video 1/1 (79/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.2ms
video 1/1 (80/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.7ms
video 1/1 (81/124) /content/쓰레기.mp4: 384x640 3 plastics, 5 papers, 11.3ms
video 1/1 (82/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.2ms
video 1/1 (83/124) /content/쓰레기.mp4: 384x640 1 plastic, 6 papers, 12.9ms
video 1/1 (84/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.3ms
video 1/1 (85/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.8ms
video 1/1 (86/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 12.3ms
video 1/1 (87/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.7ms
video 1/1 (88/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.2ms
video 1/1 (89/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 13.4ms
video 1/1 (90/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 12.7ms
video 1/1 (91/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms
video 1/1 (92/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms
video 1/1 (93/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms
video 1/1 (94/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.3ms
video 1/1 (95/124) /content/쓰레기.mp4: 384x640 2 plastics, 6 papers, 11.4ms
video 1/1 (96/124) /content/쓰레기.mp4: 384x640 6 papers, 11.3ms
video 1/1 (97/124) /content/쓰레기.mp4: 384x640 3 plastics, 5 papers, 11.2ms
video 1/1 (98/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.2ms
video 1/1 (99/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.4ms
video 1/1 (100/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (101/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.2ms
video 1/1 (102/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.2ms
video 1/1 (103/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 11.6ms
video 1/1 (104/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.5ms
video 1/1 (105/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 13.5ms
video 1/1 (106/124) /content/쓰레기.mp4: 384x640 1 plastic, 5 papers, 12.5ms
video 1/1 (107/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.1ms
video 1/1 (108/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.3ms
video 1/1 (109/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 11.2ms
video 1/1 (110/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms
video 1/1 (111/124) /content/쓰레기.mp4: 384x640 2 plastics, 5 papers, 11.4ms
video 1/1 (112/124) /content/쓰레기.mp4: 384x640 2 plastics, 4 papers, 12.0ms
video 1/1 (113/124) /content/쓰레기.mp4: 384x640 2 plastics, 3 papers, 11.3ms
video 1/1 (114/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.7ms
video 1/1 (115/124) /content/쓰레기.mp4: 384x640 1 plastic, 2 papers, 11.2ms
video 1/1 (116/124) /content/쓰레기.mp4: 384x640 1 plastic, 4 papers, 11.8ms
video 1/1 (117/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms
video 1/1 (118/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.3ms
video 1/1 (119/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.5ms
video 1/1 (120/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.5ms
video 1/1 (121/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 12.0ms
video 1/1 (122/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 10.9ms
video 1/1 (123/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 10.9ms
video 1/1 (124/124) /content/쓰레기.mp4: 384x640 1 plastic, 3 papers, 11.2ms
Speed: 0.5ms pre-process, 11.4ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp2
In [ ]:
!python detect.py --weights runs/train/exp3/weights/best.pt --img 640 --conf 0.3 --source /content/쓰레기2.mp4 
detect: weights=['runs/train/exp3/weights/best.pt'], source=/content/쓰레기2.mp4, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.3, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1
YOLOv5 🚀 v7.0-46-g96a71b1 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (A100-SXM4-40GB, 40536MiB)

Fusing layers... 
Model summary: 157 layers, 7034398 parameters, 0 gradients, 15.8 GFLOPs
video 1/1 (1/168) /content/쓰레기2.mp4: 640x384 1 paper, 17.2ms
video 1/1 (2/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.6ms
video 1/1 (3/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (4/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (5/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (6/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (7/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (8/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms
video 1/1 (9/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (10/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.3ms
video 1/1 (11/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.2ms
video 1/1 (12/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.2ms
video 1/1 (13/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.8ms
video 1/1 (14/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms
video 1/1 (15/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms
video 1/1 (16/168) /content/쓰레기2.mp4: 640x384 2 plastics, 1 paper, 10.7ms
video 1/1 (17/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.4ms
video 1/1 (18/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms
video 1/1 (19/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.5ms
video 1/1 (20/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (21/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (22/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (23/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (24/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (25/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (26/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (27/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (28/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (29/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (30/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (31/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms
video 1/1 (32/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (33/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.8ms
video 1/1 (34/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (35/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (36/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (37/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (38/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (39/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms
video 1/1 (40/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms
video 1/1 (41/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms
video 1/1 (42/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms
video 1/1 (43/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms
video 1/1 (44/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (45/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms
video 1/1 (46/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (47/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (48/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (49/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (50/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (51/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (52/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (53/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (54/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (55/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (56/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (57/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (58/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (59/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms
video 1/1 (60/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (61/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (62/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (63/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (64/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (65/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (66/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (67/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (68/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms
video 1/1 (69/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (70/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (71/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (72/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.3ms
video 1/1 (73/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (74/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.1ms
video 1/1 (75/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms
video 1/1 (76/168) /content/쓰레기2.mp4: 640x384 1 paper, 12.0ms
video 1/1 (77/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (78/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (79/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (80/168) /content/쓰레기2.mp4: 640x384 1 paper, 11.8ms
video 1/1 (81/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (82/168) /content/쓰레기2.mp4: 640x384 1 plastic, 1 paper, 10.3ms
video 1/1 (83/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (84/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms
video 1/1 (85/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (86/168) /content/쓰레기2.mp4: 640x384 (no detections), 12.1ms
video 1/1 (87/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (88/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (89/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (90/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (91/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (92/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (93/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (94/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (95/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (96/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (97/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (98/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (99/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (100/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (101/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (102/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (103/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (104/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (105/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (106/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (107/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (108/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (109/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (110/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.1ms
video 1/1 (111/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (112/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (113/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (114/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (115/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.2ms
video 1/1 (116/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (117/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (118/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (119/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (120/168) /content/쓰레기2.mp4: 640x384 1 paper, 11.9ms
video 1/1 (121/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (122/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (123/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (124/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms
video 1/1 (125/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (126/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (127/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.8ms
video 1/1 (128/168) /content/쓰레기2.mp4: 640x384 1 paper, 12.4ms
video 1/1 (129/168) /content/쓰레기2.mp4: 640x384 (no detections), 11.7ms
video 1/1 (130/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.6ms
video 1/1 (131/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.7ms
video 1/1 (132/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.8ms
video 1/1 (133/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (134/168) /content/쓰레기2.mp4: 640x384 1 plastic, 10.4ms
video 1/1 (135/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (136/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (137/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (138/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (139/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (140/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (141/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.5ms
video 1/1 (142/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (143/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (144/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (145/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (146/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (147/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (148/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.4ms
video 1/1 (149/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.6ms
video 1/1 (150/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.7ms
video 1/1 (151/168) /content/쓰레기2.mp4: 640x384 (no detections), 10.3ms
video 1/1 (152/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.2ms
video 1/1 (153/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
video 1/1 (154/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.6ms
video 1/1 (155/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (156/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (157/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (158/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.4ms
video 1/1 (159/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (160/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.1ms
video 1/1 (161/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.1ms
video 1/1 (162/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.5ms
video 1/1 (163/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.7ms
video 1/1 (164/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.3ms
video 1/1 (165/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.3ms
video 1/1 (166/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.5ms
video 1/1 (167/168) /content/쓰레기2.mp4: 640x384 2 papers, 10.4ms
video 1/1 (168/168) /content/쓰레기2.mp4: 640x384 1 paper, 10.5ms
Speed: 0.4ms pre-process, 10.5ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp3
In [ ]: